Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

In this paper, we propose a simple unsupervised graph representation learning method to
conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss …

As an early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) is able to be
detected by analyzing the brain connectivity networks. For this reason, we devise a new …

Previous multi-view spectral clustering methods are a two-step strategy, which first learns a
fixed common representation (or common affinity matrix) of all the views from original data …

The KNN algorithm is one of the most popular data mining algorithms. It has been widely
and successfully applied to data analysis applications across a variety of research topics in …

Incomplete multi-view clustering (IMC) aims to integrate the complementary information from
incomplete views to improve clustering performance. Most existing IMC methods try to fill the …

Automatic detection and counting of vehicles in a video is a challenging task and has
become a key application area of traffic monitoring and management. In this paper, an …

Abstract KNN (K Nearest Neighbors) classification is one of top-10 data mining algorithms. It
is significant to extend KNN classifiers sensitive to costs for imbalanced data classification …

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …